Peter Morgan@pedro3000

We designed four agents to automate the SDLC. These were: Requirements Agent: Understand requirements from requirements doc Design Agent: Create high level design doc Software Development Agent: Generate codebase to build PoC (small project) Code Test Agent: Generate code tests The workflow steps we followed were as follows: a. Requirements Gathering Task: Extract key requirements from a document. Goal: Create a concise summary of the CRM system's required features. Outcome: Defines the project scope (authentication, CRUD operations, task management, reporting). b. High-Level System Design Task: Design the architecture of the CRM system. Diagrams Generated: Use Case Diagram Class Diagram Entity-Relationship Diagram (ERD) UI Design for Dashboard Outcome: A document detailing the architecture, components, and visual diagrams of the system. c. Code Generation Task: Develop the Code for the system. Goal: Create functional code that implements core features. Outcome: Working code implementing authentication, database operations, and reporting. d. Code Testing Task: Run test cases to verify code functionality. Goal: Ensure the system meets the requirements and works as expected. Outcome: A detailed test report highlighting results and potential issues. Future Work could include: Improvements in Design Diagrams: Explore more AI-driven tools for automated generation of detailed design diagrams. Customization: Enable more advanced configurations for tasks such as adding new agents or expanding CRM functionality. Deployment: Plan for deployment of the final CRM system in a production environment. We also considered building an agentic workflow for MLOps, but ultimately decided on the SDLC.

A tool for large enterprises that allows distributed teams to collaborate on coding projects with AI-enhanced support. Replit provides the cloud-based environment for real-time collaboration, Cursor optimizes team efficiency with predictive code generation and error detection, and Claude acts as a Tech Lead, helping with code reviews, suggesting optimizations based on the team's previous code, and ensuring the team follows best practices. Multiple developers work together in Replit, with Cursor helping teams refactor, debug, and enhance their code. Claude can provide high-level oversight, such as suggesting improvements based on the overall codebase, offering advice on architecture or optimization, and ensuring security and compliance protocols are followed. This tool would significantly enhance team productivity and code quality, ensuring consistency and efficiency across large development projects. It would be especially valuable for enterprises that rely on remote or distributed teams.

We present a general biological research agent designed to accelerate discoveries in biology, medicine, and cancer research. Our agent combines a powerful Python-based backend with an intuitive chatbot front end, creating a seamless interface for researchers to interact with complex computational tools using natural language. This project demonstrates how artificial intelligence can streamline research processes, from literature mining to data analysis and hypothesis generation. By integrating advanced natural language processing models and machine learning algorithms, the agent assists researchers in navigating vast scientific literature and data. The platform can process large datasets, extract pertinent information, and provide context-aware responses to complex queries. The Python backend leverages robust computational libraries, ensuring efficient data handling and analysis, while the chatbot interface UI allows users to engage conversationally, lowering the barrier to entry for those without extensive technical expertise. One key feature is advanced literature mining; the agent performs comprehensive searches across databases like PubMed Central [1] and arXiv [2]. Utilizing natural language processing models, it extracts key findings, summarizes articles, and identifies emerging trends, helping researchers stay updated with the latest developments. Our general biological research agent represents a significant advancement in integrating artificial intelligence into biomedical research. We are excited about the possibilities this tool presents and look forward to refining it further, integrating new features, and collaborating with the research community to maximize its impact. [1] PubMed Central, https://www.ncbi.nlm.nih.gov/pmc/ [2] arXiv, https://arxiv.org/.